
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ChineseCLIPImageProcessor




class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  def __init__(					self     :					Tuple     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					Tuple=7     ,				UpperCamelCase__     :					Optional[Any]=3     ,				UpperCamelCase__     :					Optional[Any]=18     ,				UpperCamelCase__     :					str=30     ,				UpperCamelCase__     :					List[str]=400     ,				UpperCamelCase__     :					Optional[Any]=True     ,				UpperCamelCase__     :					Union[str, Any]=None     ,				UpperCamelCase__     :					Dict=True     ,				UpperCamelCase__     :					Tuple=None     ,				UpperCamelCase__     :					Union[str, Any]=True     ,				UpperCamelCase__     :					Optional[int]=[0.48145466, 0.4578275, 0.40821073]     ,				UpperCamelCase__     :					str=[0.26862954, 0.26130258, 0.27577711]     ,				UpperCamelCase__     :					Union[str, Any]=True     ,				)     ->      int:







    """simple docstring"""






    __magic_name__								=			size if size is not None else {"""height""": 224, """width""": 224}
    __magic_name__								=			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
    __magic_name__								=			parent
    __magic_name__								=			batch_size
    __magic_name__								=			num_channels
    __magic_name__								=			image_size
    __magic_name__								=			min_resolution
    __magic_name__								=			max_resolution
    __magic_name__								=			do_resize
    __magic_name__								=			size
    __magic_name__								=			do_center_crop
    __magic_name__								=			crop_size
    __magic_name__								=			do_normalize
    __magic_name__								=			image_mean
    __magic_name__								=			image_std
    __magic_name__								=			do_convert_rgb
  def 				_lowercase				(					self     :					Tuple       )     ->      Union[str, Any]:







    """simple docstring"""






    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_convert_rgb": self.do_convert_rgb,
    }





  def 				_lowercase				(					self     :					List[str]     ,				UpperCamelCase__     :					Union[str, Any]=False     ,				UpperCamelCase__     :					List[str]=False     ,				UpperCamelCase__     :					Any=False       )     ->      List[Any]:







    """simple docstring"""






    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    if equal_resolution:
      __magic_name__								=			[]
      for i in range(self.batch_size       ):
        image_inputs.append(
            np.random.randint(
                255     ,				size=(self.num_channels, self.max_resolution, self.max_resolution)     ,				dtype=np.uinta       )       )
    else:
      __magic_name__								=			[]
      for i in range(self.batch_size       ):
        __magic_name__	,       __magic_name__								=			np.random.choice(np.arange(self.min_resolution     ,				self.max_resolution       )     ,				2       )
        image_inputs.append(np.random.randint(255     ,				size=(self.num_channels, width, height)     ,				dtype=np.uinta       )       )

    if not numpify and not torchify:
      # PIL expects the channel dimension as last dimension
      __magic_name__								=			[Image.fromarray(np.moveaxis(UpperCamelCase__     ,				0     ,				-1       )       ) for x in image_inputs]

    if torchify:
      __magic_name__								=			[torch.from_numpy(UpperCamelCase__       ) for x in image_inputs]

    return image_inputs







@require_torch
@require_vision
class 			UpperCAmelCase_     (					_A    ,			unittest.TestCase	):


  '''simple docstring'''







  a__       			=						ChineseCLIPImageProcessor if is_vision_available() else None
  def 				_lowercase				(					self     :					Tuple       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			ChineseCLIPImageProcessingTester(self     ,				do_center_crop=UpperCamelCase__       )
  @property
  def 				_lowercase				(					self     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    return self.image_processor_tester.prepare_image_processor_dict()
  def 				_lowercase				(					self     :					Tuple       )     ->      int:







    """simple docstring"""






    __magic_name__								=			self.image_processing_class(**self.image_processor_dict       )
    self.assertTrue(hasattr(UpperCamelCase__     ,				"""do_resize"""       )       )
    self.assertTrue(hasattr(UpperCamelCase__     ,				"""size"""       )       )
    self.assertTrue(hasattr(UpperCamelCase__     ,				"""do_center_crop"""       )       )
    self.assertTrue(hasattr(UpperCamelCase__     ,				"""center_crop"""       )       )
    self.assertTrue(hasattr(UpperCamelCase__     ,				"""do_normalize"""       )       )
    self.assertTrue(hasattr(UpperCamelCase__     ,				"""image_mean"""       )       )
    self.assertTrue(hasattr(UpperCamelCase__     ,				"""image_std"""       )       )
    self.assertTrue(hasattr(UpperCamelCase__     ,				"""do_convert_rgb"""       )       )
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.image_processing_class.from_dict(self.image_processor_dict       )
    self.assertEqual(image_processor.size     ,				{"""height""": 224, """width""": 224}       )
    self.assertEqual(image_processor.crop_size     ,				{"""height""": 18, """width""": 18}       )

    __magic_name__								=			self.image_processing_class.from_dict(self.image_processor_dict     ,				size=42     ,				crop_size=84       )
    self.assertEqual(image_processor.size     ,				{"""shortest_edge""": 42}       )
    self.assertEqual(image_processor.crop_size     ,				{"""height""": 84, """width""": 84}       )
  def 				_lowercase				(					self     :					Tuple       )     ->      List[Any]:







    """simple docstring"""






    pass
  def 				_lowercase				(					self     :					Tuple       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			self.image_processing_class(**self.image_processor_dict       )
    # create random PIL images
    __magic_name__								=			self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__       )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__     ,				Image.Image       )

    # Test not batched input
    __magic_name__								=			image_processing(image_inputs[0]     ,				return_tensors="""pt"""       ).pixel_values
    self.assertEqual(
        encoded_images.shape     ,				(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )     ,				)

    # Test batched
    __magic_name__								=			image_processing(UpperCamelCase__     ,				return_tensors="""pt"""       ).pixel_values
    self.assertEqual(
        encoded_images.shape     ,				(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )     ,				)
  def 				_lowercase				(					self     :					List[Any]       )     ->      List[Any]:







    """simple docstring"""






    __magic_name__								=			self.image_processing_class(**self.image_processor_dict       )
    # create random numpy tensors
    __magic_name__								=			self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__     ,				numpify=UpperCamelCase__       )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__     ,				np.ndarray       )

    # Test not batched input
    __magic_name__								=			image_processing(image_inputs[0]     ,				return_tensors="""pt"""       ).pixel_values
    self.assertEqual(
        encoded_images.shape     ,				(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )     ,				)

    # Test batched
    __magic_name__								=			image_processing(UpperCamelCase__     ,				return_tensors="""pt"""       ).pixel_values
    self.assertEqual(
        encoded_images.shape     ,				(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )     ,				)





  def 				_lowercase				(					self     :					List[Any]       )     ->      int:







    """simple docstring"""






    __magic_name__								=			self.image_processing_class(**self.image_processor_dict       )
    # create random PyTorch tensors
    __magic_name__								=			self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__     ,				torchify=UpperCamelCase__       )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__     ,				torch.Tensor       )

    # Test not batched input
    __magic_name__								=			image_processing(image_inputs[0]     ,				return_tensors="""pt"""       ).pixel_values
    self.assertEqual(
        encoded_images.shape     ,				(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )     ,				)

    # Test batched
    __magic_name__								=			image_processing(UpperCamelCase__     ,				return_tensors="""pt"""       ).pixel_values
    self.assertEqual(
        encoded_images.shape     ,				(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )     ,				)







@require_torch
@require_vision
class 			UpperCAmelCase_     (					_A    ,			unittest.TestCase	):


  '''simple docstring'''







  a__       			=						ChineseCLIPImageProcessor if is_vision_available() else None
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			ChineseCLIPImageProcessingTester(self     ,				num_channels=4     ,				do_center_crop=UpperCamelCase__       )
    __magic_name__								=			3
  @property
  def 				_lowercase				(					self     :					List[str]       )     ->      Optional[Any]:







    """simple docstring"""






    return self.image_processor_tester.prepare_image_processor_dict()
  def 				_lowercase				(					self     :					Dict       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			self.image_processing_class(**self.image_processor_dict       )
    self.assertTrue(hasattr(UpperCamelCase__     ,				"""do_resize"""       )       )
    self.assertTrue(hasattr(UpperCamelCase__     ,				"""size"""       )       )
    self.assertTrue(hasattr(UpperCamelCase__     ,				"""do_center_crop"""       )       )
    self.assertTrue(hasattr(UpperCamelCase__     ,				"""center_crop"""       )       )
    self.assertTrue(hasattr(UpperCamelCase__     ,				"""do_normalize"""       )       )
    self.assertTrue(hasattr(UpperCamelCase__     ,				"""image_mean"""       )       )
    self.assertTrue(hasattr(UpperCamelCase__     ,				"""image_std"""       )       )
    self.assertTrue(hasattr(UpperCamelCase__     ,				"""do_convert_rgb"""       )       )
  def 				_lowercase				(					self     :					Dict       )     ->      int:







    """simple docstring"""






    pass





  def 				_lowercase				(					self     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    __magic_name__								=			self.image_processing_class(**self.image_processor_dict       )
    # create random PIL images
    __magic_name__								=			self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__       )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__     ,				Image.Image       )

    # Test not batched input
    __magic_name__								=			image_processing(image_inputs[0]     ,				return_tensors="""pt"""       ).pixel_values
    self.assertEqual(
        encoded_images.shape     ,				(
            1,
            self.expected_encoded_image_num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )     ,				)

    # Test batched
    __magic_name__								=			image_processing(UpperCamelCase__     ,				return_tensors="""pt"""       ).pixel_values
    self.assertEqual(
        encoded_images.shape     ,				(
            self.image_processor_tester.batch_size,
            self.expected_encoded_image_num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )     ,				)



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						["""image_processor""", """tokenizer"""]
  a__       			=						"""BridgeTowerImageProcessor"""
  a__       			=						("""RobertaTokenizer""", """RobertaTokenizerFast""")
  def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    super().__init__(UpperCamelCase__     ,				UpperCamelCase__       )
  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Union[bool, str, PaddingStrategy] = False     ,				UpperCamelCase__     :					Union[bool, str, TruncationStrategy] = None     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					int = 0     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Optional[Union[str, TensorType]] = None     ,				**UpperCamelCase__     :					Dict     ,				)     ->      BatchEncoding:







    """simple docstring"""






    __magic_name__								=			self.tokenizer(
        text=UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				stride=UpperCamelCase__     ,				pad_to_multiple_of=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__     ,				return_attention_mask=UpperCamelCase__     ,				return_overflowing_tokens=UpperCamelCase__     ,				return_special_tokens_mask=UpperCamelCase__     ,				return_offsets_mapping=UpperCamelCase__     ,				return_length=UpperCamelCase__     ,				verbose=UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				**UpperCamelCase__     ,				)
    # add pixel_values + pixel_mask
    __magic_name__								=			self.image_processor(
        UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				do_normalize=UpperCamelCase__     ,				do_center_crop=UpperCamelCase__     ,				**UpperCamelCase__       )
    encoding.update(UpperCamelCase__       )

    return encoding
  def 				_lowercase				(					self     :					Optional[Any]     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					str       )     ->      str:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__     ,				**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				*UpperCamelCase__     :					int     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      str:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__     ,				**UpperCamelCase__       )





  @property
  def 				_lowercase				(					self     :					Tuple       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.tokenizer.model_input_names
    __magic_name__								=			self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )

